Margin of error is your “reality buffer” for survey results. It tells you how much a number might move just because you surveyed a sample (not every single person).
Margin of error is usually reported as “±X%” at a given confidence level (often 95%). It’s the range around the estimate you’d expect from sampling alone.
Your survey says 50% of customers prefer option A, with a margin of error of ±3%.
The practical interpretation is: the true value is likely between 47% and 53% (at the stated confidence level).
If option A is 48% and option B is 43%, it looks like A is “ahead” by 5 points. But each number has its own margin of error—so the uncertainty around the difference is larger.
One rule of thumb from polling explanations is that the margin of error for the difference between two numbers can be roughly double the per‑estimate margin (e.g., ±6% when each is ±3%), meaning a small lead may not be statistically meaningful.
If stakeholders want decisions based on smaller changes (like a 1–2% shift), you usually need a larger sample to reduce the margin of error.
This is why many teams pair margin of error planning with sample-size planning.
Two big levers are sample size and confidence level: larger samples typically reduce margin of error, and higher confidence levels typically increase it.